The project is based on Window10+Python3.6 environment, and uses Python libraries such as OpenCV, Sklearn and PyQt5 to construct a relatively complete gesture recognition and translation system, which can recognize all kinds of static gesture signals in life, and translate them into Chinese or Arabic numerals through image processing.Due to the limitation of the training amount of Support Vector Machines (SVM), this design is only used to recognize gesture actions 1-10, and the interface is designed using PyQt5 for real-time display of the results of gesture recognition translation.The paper focuses on the noise elimination, contour extraction, RGB colorspace and YCrCb feasibility comparison of still images of 1-10 gesture features extracted by computer camera, GUI page design, Fourier operator extraction of gestures, training SVM model, and debugging of the system. The system is also able to be integrated on different development boards and can be embedded in device carriers to meet multi-scene adaptability.
{"title":"Design of gesture recognition system based on machine vision","authors":"Weiquan Chen, Jichao Yan, Shufen Huang, L.G. Tan","doi":"10.1117/12.2667314","DOIUrl":"https://doi.org/10.1117/12.2667314","url":null,"abstract":"The project is based on Window10+Python3.6 environment, and uses Python libraries such as OpenCV, Sklearn and PyQt5 to construct a relatively complete gesture recognition and translation system, which can recognize all kinds of static gesture signals in life, and translate them into Chinese or Arabic numerals through image processing.Due to the limitation of the training amount of Support Vector Machines (SVM), this design is only used to recognize gesture actions 1-10, and the interface is designed using PyQt5 for real-time display of the results of gesture recognition translation.The paper focuses on the noise elimination, contour extraction, RGB colorspace and YCrCb feasibility comparison of still images of 1-10 gesture features extracted by computer camera, GUI page design, Fourier operator extraction of gestures, training SVM model, and debugging of the system. The system is also able to be integrated on different development boards and can be embedded in device carriers to meet multi-scene adaptability.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the background of changing angle and multi-dimensional posture overlapping, it is necessary to extract and recognize the emotion of multi-modal face images, so as to improve the expression ability of facial emotion. A method of emotion recognition of multi-modal face images under changing angle and multi-dimensional posture overlapping background based on convolution neural network and morphological feature parameter recognition is proposed. Constructing a face feature collection model with variable angle and multi-dimensional posture overlapping background, performing fusion filtering on the collected face images with variable angle and multi-dimensional posture overlapping background, extracting the edge contour feature quantity of the face images with variable angle and multi-dimensional posture overlapping background, and filtering and denoising the original image by using morphological convolution neural network transformation method, Multi-modal wavelet scale decomposition method is used to decompose the emotion features of multi-modal face images under the background of changing angles and multi-dimensional postures, and the detection model of pixel points and similarity features of multi-modal face images under the background of changing angles and multi-dimensional postures is constructed. Morphological convolution neural network transformation method is used to transform the emotion features of multi-modal face images, and edge corner detection and expression feature point clustering analysis are combined to realize the emotion recognition of multi-modal face images. The simulation results show that this method has good performance in feature extraction and clustering of multimodal facial image emotion recognition, good expression ability of facial emotion and high image quality.
{"title":"Emotion recognition of multimodal face images based on convolutional neural network","authors":"Minli Wen","doi":"10.1117/12.2667333","DOIUrl":"https://doi.org/10.1117/12.2667333","url":null,"abstract":"In the background of changing angle and multi-dimensional posture overlapping, it is necessary to extract and recognize the emotion of multi-modal face images, so as to improve the expression ability of facial emotion. A method of emotion recognition of multi-modal face images under changing angle and multi-dimensional posture overlapping background based on convolution neural network and morphological feature parameter recognition is proposed. Constructing a face feature collection model with variable angle and multi-dimensional posture overlapping background, performing fusion filtering on the collected face images with variable angle and multi-dimensional posture overlapping background, extracting the edge contour feature quantity of the face images with variable angle and multi-dimensional posture overlapping background, and filtering and denoising the original image by using morphological convolution neural network transformation method, Multi-modal wavelet scale decomposition method is used to decompose the emotion features of multi-modal face images under the background of changing angles and multi-dimensional postures, and the detection model of pixel points and similarity features of multi-modal face images under the background of changing angles and multi-dimensional postures is constructed. Morphological convolution neural network transformation method is used to transform the emotion features of multi-modal face images, and edge corner detection and expression feature point clustering analysis are combined to realize the emotion recognition of multi-modal face images. The simulation results show that this method has good performance in feature extraction and clustering of multimodal facial image emotion recognition, good expression ability of facial emotion and high image quality.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increase in energy demand, carbon emissions, environmental pollution, climate change and other issues have become increasingly prominent, China has accelerated the construction of new energy sources. Especially in the field of wind power generation, it is the most potential type of large-scale development of non-hydroelectric renewable energy. Due to the volatility, intermittency and low energy density of wind power, the power of wind power also fluctuates. However, with the early digital transformation of my country's energy industry, a large number of meteorological environments and equipment measurement points have been accumulated in wind power production sites. Power generation related data, using artificial intelligence, deep learning and other technologies can effectively predict the power generation of the station with high precision. A model algorithm of multi-loop gradient boosting decision tree is used in this paper, considering the stationarity test of time series and wind power fluctuation attribute, the accuracy of wind power prediction is effectively improved. Help the power dispatching department to pre-arrange dispatching plans according to wind power changes. Ensure the smooth and safe operation of the power grid.
{"title":"Wind power prediction method based on multi-loop improved gradient boosting decision tree","authors":"Zheng He, Lin Xu, Yufei Ai, Wei Li, Huanhuan Dong","doi":"10.1117/12.2667668","DOIUrl":"https://doi.org/10.1117/12.2667668","url":null,"abstract":"With the increase in energy demand, carbon emissions, environmental pollution, climate change and other issues have become increasingly prominent, China has accelerated the construction of new energy sources. Especially in the field of wind power generation, it is the most potential type of large-scale development of non-hydroelectric renewable energy. Due to the volatility, intermittency and low energy density of wind power, the power of wind power also fluctuates. However, with the early digital transformation of my country's energy industry, a large number of meteorological environments and equipment measurement points have been accumulated in wind power production sites. Power generation related data, using artificial intelligence, deep learning and other technologies can effectively predict the power generation of the station with high precision. A model algorithm of multi-loop gradient boosting decision tree is used in this paper, considering the stationarity test of time series and wind power fluctuation attribute, the accuracy of wind power prediction is effectively improved. Help the power dispatching department to pre-arrange dispatching plans according to wind power changes. Ensure the smooth and safe operation of the power grid.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131574864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The essence of enjoying music is that people can track music beat anytime and be brought into the scene expressed by music. Music beat tracking is a common task in Music Information Retrieve (MIR). While numerous studies have been done in this field, most works focus on the offline beat tracking. However, tracking music beat in real time is a challenging task for computers. In the past few years, people attach more importance to this field. Researchers care about the music beat without taking music style or context into consideration. In this paper, we propose a method for tracking music beats in real time in conjunction with music genre. Specifically, the proposed model is based on a widely-used framework of Hidden Markov Model (HMM). By recognizing the genre of input music, we narrow the range of beats per minute (BPM), which significantly reduces the number of hidden states in HMM. Consequently, the inference time of beat tracking decreases. We experimentally verify the model on the open-source Ballroom dataset, and its accuracy remains at a competitive level while having a much shorter inference time.
{"title":"Accelerating real-time music beat tracking based on hidden Markov model by using genre information","authors":"Liangfeng Zhou, Guangxiao Song, Zhi-jian Wang, Meng Xia","doi":"10.1117/12.2667472","DOIUrl":"https://doi.org/10.1117/12.2667472","url":null,"abstract":"The essence of enjoying music is that people can track music beat anytime and be brought into the scene expressed by music. Music beat tracking is a common task in Music Information Retrieve (MIR). While numerous studies have been done in this field, most works focus on the offline beat tracking. However, tracking music beat in real time is a challenging task for computers. In the past few years, people attach more importance to this field. Researchers care about the music beat without taking music style or context into consideration. In this paper, we propose a method for tracking music beats in real time in conjunction with music genre. Specifically, the proposed model is based on a widely-used framework of Hidden Markov Model (HMM). By recognizing the genre of input music, we narrow the range of beats per minute (BPM), which significantly reduces the number of hidden states in HMM. Consequently, the inference time of beat tracking decreases. We experimentally verify the model on the open-source Ballroom dataset, and its accuracy remains at a competitive level while having a much shorter inference time.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134433792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zilin Zeng, Junwei Wang, Zhigang Hu, Dongnan Su, Peng Shang
In this paper, a novel policy network update approach based on Proximal Policy Optimization (PPO), Advantageous Update Policy Proximal Policy Optimization (AUP-PPO), is proposed to alleviate the problem of over-fitting caused by the use of shared layers for policy and value functions. Extended from the previous sample-efficient reinforcement learning method PPO that uses separate networks to learn policy and value functions to make them decouple optimization, AUP-PPO uses the value function to calculate the advantage and updates the policy with the loss between the current and target advantage function as a penalty term instead of the value function. Evaluated by multiple benchmark control tasks in Open-AI gym, AUP-PPO exhibits better generalization to the environment and achieves faster convergence and better robustness compared with the original PPO.
{"title":"Advantage policy update based on proximal policy optimization","authors":"Zilin Zeng, Junwei Wang, Zhigang Hu, Dongnan Su, Peng Shang","doi":"10.1117/12.2667235","DOIUrl":"https://doi.org/10.1117/12.2667235","url":null,"abstract":"In this paper, a novel policy network update approach based on Proximal Policy Optimization (PPO), Advantageous Update Policy Proximal Policy Optimization (AUP-PPO), is proposed to alleviate the problem of over-fitting caused by the use of shared layers for policy and value functions. Extended from the previous sample-efficient reinforcement learning method PPO that uses separate networks to learn policy and value functions to make them decouple optimization, AUP-PPO uses the value function to calculate the advantage and updates the policy with the loss between the current and target advantage function as a penalty term instead of the value function. Evaluated by multiple benchmark control tasks in Open-AI gym, AUP-PPO exhibits better generalization to the environment and achieves faster convergence and better robustness compared with the original PPO.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131033673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Liu, Zhiqiang Wang, Fengjing Zhang, Jun Xie, Zhaohong Xu
In order to obtain the scale information of ship targets effectively, we proposed an improved Faster R-CNN algorithm which integrated multi-scale region proposal and pooling of ROI, visual attention mechanism and rotation region regression and suppression, and ship targets can be positioned by the rotate quadrangle bounding boxes to obtain the scale information of them. Our improved model is based on the standard Faster R-CNN and is maintained through end-to-end training.
{"title":"Target scale information detection based on improved Faster R-CNN","authors":"Yu Liu, Zhiqiang Wang, Fengjing Zhang, Jun Xie, Zhaohong Xu","doi":"10.1117/12.2667260","DOIUrl":"https://doi.org/10.1117/12.2667260","url":null,"abstract":"In order to obtain the scale information of ship targets effectively, we proposed an improved Faster R-CNN algorithm which integrated multi-scale region proposal and pooling of ROI, visual attention mechanism and rotation region regression and suppression, and ship targets can be positioned by the rotate quadrangle bounding boxes to obtain the scale information of them. Our improved model is based on the standard Faster R-CNN and is maintained through end-to-end training.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132126119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For the rail traffic, feasibility of 5th Generation (5G) mobile communication massive Multiple Input Multiple Output (MIMO) used in the tunnel is studying. In order to effectively set up base station, antenna and intelligent reflecting surface (IRS), the direction of arrival (DOA) must be captured. Aiming at the problem of poor performance of two-dimensional MUltiple SIgnal Classification (2D-MUSIC) algorithm in the environment of low signal-to-noise ratio (SNR), small snapshots and small incident angle interval signals, an improved MUSIC algorithm with uniform rectangular array (URA) based on reconstructive subspace is proposed. By reconstructing subspace, a new spatial spectrum is obtained in terms of subspace eigenvectors. Then, the DOAs are obtained by searching the maximum of the new spatial spectrum. High estimation accuracy and angular resolution are often required in practical applications of rail traffic 5G system, and there exist tunnel scenarios that we need the improved MUSIC algorithm has the ability to conduct two-dimensional DOA estimation. Simulations and the measured data of straight tunnel scenario are used to verify the effectiveness of the proposed algorithm and its higher searching precision in complex signal environments such as low SNR, strong-to-weak proximity, and coherent interference.
{"title":"DOA estimation of rail transit tunnel far field signals based on reconstructive subspace MUSIC","authors":"Yanliang Jin, Rukun Lyu, Yuan Gao, Guoxing Zheng","doi":"10.1117/12.2667279","DOIUrl":"https://doi.org/10.1117/12.2667279","url":null,"abstract":"For the rail traffic, feasibility of 5th Generation (5G) mobile communication massive Multiple Input Multiple Output (MIMO) used in the tunnel is studying. In order to effectively set up base station, antenna and intelligent reflecting surface (IRS), the direction of arrival (DOA) must be captured. Aiming at the problem of poor performance of two-dimensional MUltiple SIgnal Classification (2D-MUSIC) algorithm in the environment of low signal-to-noise ratio (SNR), small snapshots and small incident angle interval signals, an improved MUSIC algorithm with uniform rectangular array (URA) based on reconstructive subspace is proposed. By reconstructing subspace, a new spatial spectrum is obtained in terms of subspace eigenvectors. Then, the DOAs are obtained by searching the maximum of the new spatial spectrum. High estimation accuracy and angular resolution are often required in practical applications of rail traffic 5G system, and there exist tunnel scenarios that we need the improved MUSIC algorithm has the ability to conduct two-dimensional DOA estimation. Simulations and the measured data of straight tunnel scenario are used to verify the effectiveness of the proposed algorithm and its higher searching precision in complex signal environments such as low SNR, strong-to-weak proximity, and coherent interference.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132144386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Feature extraction is an important research topic in the field of image processing.In autonomous driving, it is of great importance to extract the feature information of the picture obtained by the vehicle camera for the agent to better understand the environment information. In order to improve the quality of feature extraction, this paper combines meta-learning and deep learning-based feature extraction methods, and proposes a Meta-VAE-WGAN-GP (MVWP) feature extraction algorithm, and applies it to automatic driving. Firstly, aiming at the problem of parameter centralization in Wasserstein generative adversarial network (WGAN) and the problem of gradient explosion and gradient disappearance caused by improper manual parameter adjustment, a generative adversarial network based on gradient penalty and Wasserstein distance (WGAN-GP) was proposed, and it was combined with VAE. The VAE-WGAN-GP model is formed. Secondly, aiming at the problem that the feature extraction model needs to be trained from scratch every time it is faced with a new task, and the training time is too long, the MVWP model is formed by combining meta-learning with VAE-WGAN-GP (VWP) mentioned above. Finally, the experimental results show that compared with VAE, VAE-WGAN and VWP, the training speed of MVWP model is increased by about 6 times, the reconstruction loss is reduced by 55.9%, 37.8% and 20.2%, respectively, and the reconstructed images are clearer.
{"title":"Research on feature extraction algorithm based on meta-learning","authors":"Yanliang Jin, Baorong Fan, Yuan Gao","doi":"10.1117/12.2667413","DOIUrl":"https://doi.org/10.1117/12.2667413","url":null,"abstract":"Feature extraction is an important research topic in the field of image processing.In autonomous driving, it is of great importance to extract the feature information of the picture obtained by the vehicle camera for the agent to better understand the environment information. In order to improve the quality of feature extraction, this paper combines meta-learning and deep learning-based feature extraction methods, and proposes a Meta-VAE-WGAN-GP (MVWP) feature extraction algorithm, and applies it to automatic driving. Firstly, aiming at the problem of parameter centralization in Wasserstein generative adversarial network (WGAN) and the problem of gradient explosion and gradient disappearance caused by improper manual parameter adjustment, a generative adversarial network based on gradient penalty and Wasserstein distance (WGAN-GP) was proposed, and it was combined with VAE. The VAE-WGAN-GP model is formed. Secondly, aiming at the problem that the feature extraction model needs to be trained from scratch every time it is faced with a new task, and the training time is too long, the MVWP model is formed by combining meta-learning with VAE-WGAN-GP (VWP) mentioned above. Finally, the experimental results show that compared with VAE, VAE-WGAN and VWP, the training speed of MVWP model is increased by about 6 times, the reconstruction loss is reduced by 55.9%, 37.8% and 20.2%, respectively, and the reconstructed images are clearer.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124890456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the new operational styles such as mosaic warfare and multi-domain warfare of the US military moving from concept to application, the winning elements and operational modes of future maritime operations have been profoundly changed. Firstly, the military capabilities of the US military to break through anti-access/area denial are analyzed, and then the characteristics and effects of the implementation of multi-domain warfare under the mosaic warfare concept in breaking through anti-access/area denial are extracted from the military capabilities, operational styles, and practical applications. On this basis, countermeasure suggestions are proposed to deal with the implementation of multi-domain warfare under the mosaic warfare concept. The practical significance of building unmanned maritime combat clusters is explained from the perspective of military needs, and the force design, equipment development, and combat style of unmanned maritime combat clusters are described in detail. A typical combat scenario is used to show the combat process of unmanned maritime combat clusters. Finally, the key problem model of intelligent command and control decision of maritime unmanned cluster is established for this typical combat scenario, and the corresponding algorithmic framework is constructed for the input and output of the typical combat scenario, which provides research ideas for further empirical research.
{"title":"Analysis of maritime unmanned combat cluster employment in the context of anti-intervention/area denial","authors":"Jiangshan Liu, P. Pen","doi":"10.1117/12.2667516","DOIUrl":"https://doi.org/10.1117/12.2667516","url":null,"abstract":"With the new operational styles such as mosaic warfare and multi-domain warfare of the US military moving from concept to application, the winning elements and operational modes of future maritime operations have been profoundly changed. Firstly, the military capabilities of the US military to break through anti-access/area denial are analyzed, and then the characteristics and effects of the implementation of multi-domain warfare under the mosaic warfare concept in breaking through anti-access/area denial are extracted from the military capabilities, operational styles, and practical applications. On this basis, countermeasure suggestions are proposed to deal with the implementation of multi-domain warfare under the mosaic warfare concept. The practical significance of building unmanned maritime combat clusters is explained from the perspective of military needs, and the force design, equipment development, and combat style of unmanned maritime combat clusters are described in detail. A typical combat scenario is used to show the combat process of unmanned maritime combat clusters. Finally, the key problem model of intelligent command and control decision of maritime unmanned cluster is established for this typical combat scenario, and the corresponding algorithmic framework is constructed for the input and output of the typical combat scenario, which provides research ideas for further empirical research.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127893662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Micro air-launched decoy (MALD) is electronic weapon aiming to interfere with enemy air defense systems. MALD uses signal enhancement subsystems and active radar jammers as its loads. This paper discusses the basic situation of MALD in detail, and analyzes the technical advantages and development trends.
{"title":"Technology research of micro air-launched decoy","authors":"Fang-zheng Zhao, Xiao Zhang, Xin Zhang","doi":"10.1117/12.2667665","DOIUrl":"https://doi.org/10.1117/12.2667665","url":null,"abstract":"Micro air-launched decoy (MALD) is electronic weapon aiming to interfere with enemy air defense systems. MALD uses signal enhancement subsystems and active radar jammers as its loads. This paper discusses the basic situation of MALD in detail, and analyzes the technical advantages and development trends.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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